{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyecharts import Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "line = Line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from setting import get_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = get_engine('db_stock','local')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pymysql\\cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xD6\\\\xD0\\\\xB9\\\\xFA\\\\xB1\\\\xEA...' for column 'VARIABLE_VALUE' at row 480\")\n",
      "  result = self._query(query)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_sql('tb_cb_index',con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>指数</th>\n",
       "      <th>成交额(亿元)</th>\n",
       "      <th>涨跌</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>转债数目</th>\n",
       "      <th>剩余规模</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>10.581</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>883.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1009.38</td>\n",
       "      <td>14.911</td>\n",
       "      <td>9.378</td>\n",
       "      <td>0.938</td>\n",
       "      <td>37.0</td>\n",
       "      <td>885.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>1019.43</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10.055</td>\n",
       "      <td>0.996</td>\n",
       "      <td>38.0</td>\n",
       "      <td>895.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>1024.79</td>\n",
       "      <td>15.495</td>\n",
       "      <td>5.357</td>\n",
       "      <td>0.525</td>\n",
       "      <td>38.0</td>\n",
       "      <td>895.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>1034.83</td>\n",
       "      <td>19.973</td>\n",
       "      <td>10.039</td>\n",
       "      <td>0.980</td>\n",
       "      <td>38.0</td>\n",
       "      <td>895.191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期       指数  成交额(亿元)      涨跌    涨跌额  转债数目     剩余规模\n",
       "0 2017-12-29  1000.00   10.581   0.000  0.000  36.0  883.841\n",
       "1 2018-01-02  1009.38   14.911   9.378  0.938  37.0  885.991\n",
       "2 2018-01-03  1019.43   18.421  10.055  0.996  38.0  895.191\n",
       "3 2018-01-04  1024.79   15.495   5.357  0.525  38.0  895.191\n",
       "4 2018-01-05  1034.83   19.973  10.039  0.980  38.0  895.191"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 307 entries, 0 to 306\n",
      "Data columns (total 7 columns):\n",
      "日期         307 non-null datetime64[ns]\n",
      "指数         307 non-null float64\n",
      "成交额(亿元)    307 non-null float64\n",
      "涨跌         307 non-null float64\n",
      "涨跌额        307 non-null float64\n",
      "转债数目       307 non-null float64\n",
      "剩余规模       307 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(6)\n",
      "memory usage: 16.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "v=list(df['转债数目'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "dtype '<class 'datetime.date'>' not understood",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-648ff44762dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'日期'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[0;32m   5689\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5690\u001b[0m             new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,\n\u001b[1;32m-> 5691\u001b[1;33m                                          **kwargs)\n\u001b[0m\u001b[0;32m   5692\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m    529\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'astype'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[0;32m    393\u001b[0m                                             copy=align_copy)\n\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m             \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[0;32m    532\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'raise'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[1;32m--> 534\u001b[1;33m                             **kwargs)\n\u001b[0m\u001b[0;32m    535\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36m_astype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m   2126\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mon\u001b[0m \u001b[0man\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2127\u001b[0m         \"\"\"\n\u001b[1;32m-> 2128\u001b[1;33m         \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpandas_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2130\u001b[0m         \u001b[1;31m# if we are passed a datetime64[ns, tz]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\common.py\u001b[0m in \u001b[0;36mpandas_dtype\u001b[1;34m(dtype)\u001b[0m\n\u001b[0;32m   2027\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnpdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2028\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mnpdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'O'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2029\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"dtype '{}' not understood\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2030\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2031\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnpdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: dtype '<class 'datetime.date'>' not understood"
     ]
    }
   ],
   "source": [
    "df['日期'].astype(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=list(df['日期'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=list(df['日期'].dt.to_pydatetime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2017, 12, 29, 0, 0),\n",
       " datetime.datetime(2018, 1, 2, 0, 0),\n",
       " datetime.datetime(2018, 1, 3, 0, 0),\n",
       " datetime.datetime(2018, 1, 4, 0, 0),\n",
       " datetime.datetime(2018, 1, 5, 0, 0),\n",
       " datetime.datetime(2018, 1, 8, 0, 0),\n",
       " datetime.datetime(2018, 1, 9, 0, 0),\n",
       " datetime.datetime(2018, 1, 10, 0, 0),\n",
       " datetime.datetime(2018, 1, 11, 0, 0),\n",
       " datetime.datetime(2018, 1, 12, 0, 0),\n",
       " datetime.datetime(2018, 1, 15, 0, 0),\n",
       " datetime.datetime(2018, 1, 16, 0, 0),\n",
       " datetime.datetime(2018, 1, 17, 0, 0),\n",
       " datetime.datetime(2018, 1, 18, 0, 0),\n",
       " datetime.datetime(2018, 1, 19, 0, 0),\n",
       " datetime.datetime(2018, 1, 22, 0, 0),\n",
       " datetime.datetime(2018, 1, 23, 0, 0),\n",
       " datetime.datetime(2018, 1, 24, 0, 0),\n",
       " datetime.datetime(2018, 1, 25, 0, 0),\n",
       " datetime.datetime(2018, 1, 26, 0, 0),\n",
       " datetime.datetime(2018, 1, 29, 0, 0),\n",
       " datetime.datetime(2018, 1, 30, 0, 0),\n",
       " datetime.datetime(2018, 1, 31, 0, 0),\n",
       " datetime.datetime(2018, 2, 1, 0, 0),\n",
       " datetime.datetime(2018, 2, 2, 0, 0),\n",
       " datetime.datetime(2018, 2, 5, 0, 0),\n",
       " datetime.datetime(2018, 2, 6, 0, 0),\n",
       " datetime.datetime(2018, 2, 7, 0, 0),\n",
       " datetime.datetime(2018, 2, 8, 0, 0),\n",
       " datetime.datetime(2018, 2, 9, 0, 0),\n",
       " datetime.datetime(2018, 2, 12, 0, 0),\n",
       " datetime.datetime(2018, 2, 13, 0, 0),\n",
       " datetime.datetime(2018, 2, 14, 0, 0),\n",
       " datetime.datetime(2018, 2, 22, 0, 0),\n",
       " datetime.datetime(2018, 2, 23, 0, 0),\n",
       " datetime.datetime(2018, 2, 26, 0, 0),\n",
       " datetime.datetime(2018, 2, 27, 0, 0),\n",
       " datetime.datetime(2018, 2, 28, 0, 0),\n",
       " datetime.datetime(2018, 3, 1, 0, 0),\n",
       " datetime.datetime(2018, 3, 2, 0, 0),\n",
       " datetime.datetime(2018, 3, 5, 0, 0),\n",
       " datetime.datetime(2018, 3, 6, 0, 0),\n",
       " datetime.datetime(2018, 3, 7, 0, 0),\n",
       " datetime.datetime(2018, 3, 8, 0, 0),\n",
       " datetime.datetime(2018, 3, 9, 0, 0),\n",
       " datetime.datetime(2018, 3, 12, 0, 0),\n",
       " datetime.datetime(2018, 3, 13, 0, 0),\n",
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=[i.strftime('%Y-%m-%d') for i in b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       " '2019-03-26',\n",
       " '2019-03-27',\n",
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       " '2019-03-29',\n",
       " '2019-04-01',\n",
       " '2019-04-02',\n",
       " '2019-04-03',\n",
       " '2019-04-04',\n",
       " '2019-04-08']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       " numpy.datetime64('2019-04-04T00:00:00.000000000'),\n",
       " numpy.datetime64('2019-04-08T00:00:00.000000000')]"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
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       "};\n",
       "myChart_9caae2ded237425fb7d9364df304e118.setOption(option_9caae2ded237425fb7d9364df304e118);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.line.Line at 0x14c1d54d518>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    line=Line()\n",
    "    line.add('可转债个数趋势',b,v,mark_line=[\"average\"], mark_point=[\"max\", \"min\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def line_smooth() -> Line:\n",
    "    c = (\n",
    "        Line()\n",
    "        .add_xaxis(Faker.choose())\n",
    "        .add_yaxis(\"商家A\", Faker.values(), is_smooth=True)\n",
    "        .add_yaxis(\"商家B\", Faker.values(), is_smooth=True)\n",
    "        .set_global_opts(title_opts=opts.TitleOpts(title=\"Line-smooth\"))\n",
    "    )\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Line' object has no attribute 'add_xaxis'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-b243a94d2c18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mline_smooth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-36-e279273b53bc>\u001b[0m in \u001b[0;36mline_smooth\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mline_smooth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mLine\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     c = (\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mLine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[1;33m.\u001b[0m\u001b[0madd_xaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFaker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[1;33m.\u001b[0m\u001b[0madd_yaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"商家A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFaker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_smooth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Line' object has no attribute 'add_xaxis'"
     ]
    }
   ],
   "source": [
    "line_smooth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Bar' from 'pyecharts.charts' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-73ce9080304a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharts\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mbar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_xaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"衬衫\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"羊毛衫\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"雪纺衫\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"裤子\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"高跟鞋\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"袜子\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_yaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"商家A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m36\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m75\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m90\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'Bar' from 'pyecharts.charts' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from pyecharts.charts import Bar\n",
    "\n",
    "bar = Bar()\n",
    "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
    "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
    "# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件\n",
    "# 也可以传入路径参数，如 bar.render(\"mycharts.html\")\n",
    "bar.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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